LSTM-ACB-Based RA for IoT Mixed Traffic
A novel random access (RA) scheme for mixed URLLC-mMTC traffic scenario is proposed using realistic statistical models, with the use mode presenting long-term traffic regularity. The traffic is predicted by a long short-term memory neural network, which enables a traffic-aware resource slicing aided by contention access class barring-based procedure. The method combines a grant-free (GF) RA scheme with an intermediate step to congestion alleviation. The protocol trade-off is a small overhead while enabling a higher number of decoded received packets thanks to the intermediate step. Numerical results evaluate the system performance for each procedure and combined solution. A comparison with GF benchmark reveals substantial improvement in system performance.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LSTM-ACB-Based Random Access for Mixed Traffic IoT Networks
We propose a novel random access (RA) protocol that accounts for the network traffic in mixed URLLC-mMTC scenarios. By considering an IoT environment under high mMTC traffic demand, we model the traffic of each service u…
Traffic PredictionDetection of Collision-Prone Vehicle Behavior at Intersections using Siamese Interaction LSTM
As a large proportion of road accidents occur at intersections, monitoring traffic safety of intersections is important. Existing approaches are designed to investigate accidents in lane-based traffic. However, such appr…
Knowledge Distillation Neural Network for Predicting Car-following Behaviour of Human-driven and Autonomous Vehicles
As we move towards a mixed-traffic scenario of Autonomous vehicles (AVs) and Human-driven vehicles (HDVs), understanding the car-following behaviour is important to improve traffic efficiency and road safety. Using a rea…
Autonomous VehiclesDescriptiveKnowledge DistillationStacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values
Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting ha…
ImputationMissing ValuesPredictionTraffic PredictionDistributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks based on Automatic LSTM Customization and Sharing
Short-term traffic speed prediction has been an important research topic in the past decade, and many approaches have been introduced. However, providing fine-grained, accurate, and efficient traffic-speed prediction for…
Prediction